Dispatching Rule Selection Research Articles

Data envelopment analysis for dispatching rule selection

Scheduling techniques and dispatching rules are widely studied because of their strategic role in achieving optimal management of manufacturing systems. In the case of flexible manufacturing, it is essential to evaluate the effects of different dispatching rules to foresee the overall system performance. This paper addresses the issue of performance measurement and comparison of different dispatching rules with respect to several possible evaluation criteria by using data envelopment analysis (DEA). Based on simulation data retrieved from a recent study on dispatching rules in a dynamic hybrid flow shop environment, DEA methodology seems a suitable technique for ranking competing dispatching rules according to the set of selected performance criteria.

Dispatching rule selection using artificial neural networks for dynamic planning and scheduling

To schedule a job shop, the first task is to select an appropriate scheduling algorithm or rule. Because of the complexity of scheduling problems, no general algorithm sufficient for solving all scheduling problems has yet been developed. Most job-shop scheduling systems offer alternative algorithms for different situations, and experienced human schedulers are needed to select the best dispatching rule in these systems. This paper proposes a new algorithm for job-shop scheduling problems. This algorithm consists of three stages. First, computer simulation techniques are used to evaluate the efficiency of heuristic rules in different scheduling situations. Second, the simulation results are used to train a neural network in order to capture the knowledge which can be used to select the most efficient heuristic rule for each scheduling situation. Finally, the trained neural network is used as a dispatching rule selector in the real-time scheduling process. Research results have shown great potential in using a neural network to replace human schedulers in selecting an appropriate approach for real-time scheduling. This research is part of an ongoing project of developing a real-time planning and scheduling system.

Intelligent Scheduling with Machine Learning Capabilities: The Induction of Scheduling Knowledge

Abstract Dynamic scheduling of manufacturing systems has primarily involved the use of dispatching rules. In the context of conventional job shops, the relative performance of these rules has been found to depend upon the system attributes, and no single rule is dominant across all possible scenarios. This indicates die need for developing a scheduling approach which adopts a state-dependent dispatching rule selection policy. The importance of adapting the dispatching rule employed to the current state of the system is even more critical in a flexible manufacturing system because of alternative machine routing possibilities and me need for increased coordination among various machines. This study develops a framework for incorporating machine learning capabilities in intelligent scheduling. A pattern-directed method, with a built-in inductive learning module, is developed for heuristic acquisition and refinement. This method enables the scheduler to classify distinct manufacturing patterns and to generate...